Grail, Inc.

Karthik Jagadeesh

Samuel S Gross

Oliver Claude Venn

M Cyrus Maher

Anton Valouev

John Beausang

Robert Abe Paine Calef

Darya Filippova

Virgil Nicula

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Systems and methods for classifying patients with respect to multiple cancer classes

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San Francisco, CA, United States of America

Karthik Jagadeesh